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Accelerating Scientific Discovery Through Computation and Visualization

The rate of scientific discovery can be accelerated through computation and visualization. This acceleration results from the synergy of expertise, computing tools, and hardware for enabling high-performance computation, information science, and visualization that is provided by a team of computatio...

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Detalles Bibliográficos
Autores principales: Sims, James S., Hagedorn, John G., Ketcham, Peter M., Satterfield, Steven G., Griffin, Terence J., George, William L., Fowler, Howland A., am Ende, Barbara A., Hung, Howard K., Bohn, Robert B., Koontz, John E., Martys, Nicos S., Bouldin, Charles E., Warren, James A., Feder, David L., Clark, Charles W., Filla, B. James, Devaney, Judith E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: [Gaithersburg, MD] : U.S. Dept. of Commerce, National Institute of Standards and Technology 2000
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4877149/
https://www.ncbi.nlm.nih.gov/pubmed/27551642
http://dx.doi.org/10.6028/jres.105.068
Descripción
Sumario:The rate of scientific discovery can be accelerated through computation and visualization. This acceleration results from the synergy of expertise, computing tools, and hardware for enabling high-performance computation, information science, and visualization that is provided by a team of computation and visualization scientists collaborating in a peer-to-peer effort with the research scientists. In the context of this discussion, high performance refers to capabilities beyond the current state of the art in desktop computing. To be effective in this arena, a team comprising a critical mass of talent, parallel computing techniques, visualization algorithms, advanced visualization hardware, and a recurring investment is required to stay beyond the desktop capabilities. This article describes, through examples, how the Scientific Applications and Visualization Group (SAVG) at NIST has utilized high performance parallel computing and visualization to accelerate condensate modeling, (2) fluid flow in porous materials and in other complex geometries, (3) flows in suspensions, (4) x-ray absorption, (5) dielectric breakdown modeling, and (6) dendritic growth in alloys.